Overview

Dataset statistics

Number of variables20
Number of observations4601
Missing cells0
Missing cells (%)0.0%
Duplicate rows1369
Duplicate rows (%)29.8%
Total size in memory719.0 KiB
Average record size in memory160.0 B

Variable types

NUM19
BOOL1

Reproduction

Analysis started2020-08-25 01:54:16.910714
Analysis finished2020-08-25 01:55:09.280859
Duration52.37 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Dataset has 1369 (29.8%) duplicate rows Duplicates
55 is highly skewed (γ1 = 30.76499258) Skewed
3 is highly skewed (γ1 = 26.22774447) Skewed
0 has 3548 (77.1%) zeros Zeros
5 has 3602 (78.3%) zeros Zeros
30 has 4308 (93.6%) zeros Zeros
13 has 4244 (92.2%) zeros Zeros
34 has 4116 (89.5%) zeros Zeros
27 has 4138 (89.9%) zeros Zeros
31 has 4396 (95.5%) zeros Zeros
45 has 4084 (88.8%) zeros Zeros
12 has 3749 (81.5%) zeros Zeros
47 has 4398 (95.6%) zeros Zeros
3 has 4554 (99.0%) zeros Zeros
43 has 4274 (92.9%) zeros Zeros
26 has 3821 (83.0%) zeros Zeros
8 has 3828 (83.2%) zeros Zeros
17 has 3563 (77.4%) zeros Zeros
6 has 3794 (82.5%) zeros Zeros
4 has 2853 (62.0%) zeros Zeros
40 has 4453 (96.8%) zeros Zeros

Variables

0
Real number (ℝ≥0)

ZEROS

Distinct count142
Unique (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10455335796565964
Minimum0.0
Maximum4.54
Zeros3548
Zeros (%)77.1%
Memory size36.1 KiB
2020-08-25T01:55:09.326935image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.65
Maximum4.54
Range4.54
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.305357562
Coefficient of variation (CV)2.920590672
Kurtosis49.30506416
Mean0.104553358
Median Absolute Deviation (MAD)0
Skewness5.675639164
Sum481.05
Variance0.09324324068
2020-08-25T01:55:09.429944image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0354877.1%
 
0.1511.1%
 
0.09410.9%
 
0.17380.8%
 
0.08340.7%
 
0.05270.6%
 
0.07220.5%
 
0.06200.4%
 
0.34200.4%
 
0.33190.4%
 
0.43170.4%
 
0.14170.4%
 
0.12160.3%
 
0.26160.3%
 
0.16160.3%
 
0.23160.3%
 
0.19160.3%
 
0.27150.3%
 
0.11140.3%
 
0.13140.3%
 
0.18140.3%
 
0.32140.3%
 
0.15130.3%
 
0.47130.3%
 
0.46130.3%
 
Other values (117)55712.1%
 
ValueCountFrequency (%) 
0354877.1%
 
0.0140.1%
 
0.0240.1%
 
0.0340.1%
 
0.0470.2%
 
0.05270.6%
 
0.06200.4%
 
0.07220.5%
 
0.08340.7%
 
0.09410.9%
 
ValueCountFrequency (%) 
4.541< 0.1%
 
4.341< 0.1%
 
41< 0.1%
 
3.941< 0.1%
 
3.841< 0.1%
 
3.031< 0.1%
 
2.851< 0.1%
 
2.772< 0.1%
 
2.431< 0.1%
 
2.351< 0.1%
 

5
Real number (ℝ≥0)

ZEROS

Distinct count141
Unique (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09590089111062812
Minimum0.0
Maximum5.88
Zeros3602
Zeros (%)78.3%
Memory size36.1 KiB
2020-08-25T01:55:09.545477image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.63
Maximum5.88
Range5.88
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.273824083
Coefficient of variation (CV)2.855281946
Kurtosis68.445258
Mean0.09590089111
Median Absolute Deviation (MAD)0
Skewness5.956952736
Sum441.24
Variance0.07497962844
2020-08-25T01:55:09.651445image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0360278.3%
 
0.09330.7%
 
0.1320.7%
 
0.19250.5%
 
0.03250.5%
 
0.08250.5%
 
0.16240.5%
 
0.11230.5%
 
0.32210.5%
 
0.25200.4%
 
0.13200.4%
 
0.36190.4%
 
0.05190.4%
 
0.17190.4%
 
0.2180.4%
 
0.65170.4%
 
0.23170.4%
 
0.38170.4%
 
0.22170.4%
 
0.8160.3%
 
0.26160.3%
 
0.12160.3%
 
0.64150.3%
 
0.27150.3%
 
0.34140.3%
 
Other values (116)51611.2%
 
ValueCountFrequency (%) 
0360278.3%
 
0.011< 0.1%
 
0.0240.1%
 
0.03250.5%
 
0.0460.1%
 
0.05190.4%
 
0.0660.1%
 
0.0770.2%
 
0.08250.5%
 
0.09330.7%
 
ValueCountFrequency (%) 
5.881< 0.1%
 
3.571< 0.1%
 
3.441< 0.1%
 
2.941< 0.1%
 
2.631< 0.1%
 
2.541< 0.1%
 
2.431< 0.1%
 
2.32< 0.1%
 
2.12< 0.1%
 
1.8830.1%
 

30
Real number (ℝ≥0)

ZEROS

Distinct count128
Unique (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06475331449684851
Minimum0.0
Maximum12.5
Zeros4308
Zeros (%)93.6%
Memory size36.1 KiB
2020-08-25T01:55:09.760057image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.28
Maximum12.5
Range12.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4033925009
Coefficient of variation (CV)6.229681122
Kurtosis254.2325086
Mean0.0647533145
Median Absolute Deviation (MAD)0
Skewness12.66908113
Sum297.93
Variance0.1627255098
2020-08-25T01:55:10.039058image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0430893.6%
 
0.790.2%
 
0.5880.2%
 
0.2470.2%
 
4.7660.1%
 
0.2660.1%
 
0.2760.1%
 
0.3450.1%
 
0.1550.1%
 
0.2250.1%
 
0.1850.1%
 
0.5550.1%
 
0.2350.1%
 
0.3950.1%
 
0.5940.1%
 
0.4240.1%
 
0.1640.1%
 
0.8640.1%
 
0.3340.1%
 
0.5440.1%
 
0.6840.1%
 
0.3240.1%
 
0.540.1%
 
0.7330.1%
 
3.5730.1%
 
Other values (103)1743.8%
 
ValueCountFrequency (%) 
0430893.6%
 
0.0930.1%
 
0.11< 0.1%
 
0.121< 0.1%
 
0.132< 0.1%
 
0.141< 0.1%
 
0.1550.1%
 
0.1640.1%
 
0.1730.1%
 
0.1850.1%
 
ValueCountFrequency (%) 
12.51< 0.1%
 
4.7660.1%
 
4.542< 0.1%
 
4.3430.1%
 
4.1630.1%
 
41< 0.1%
 
3.841< 0.1%
 
3.5730.1%
 
3.261< 0.1%
 
3.121< 0.1%
 

13
Real number (ℝ≥0)

ZEROS

Distinct count133
Unique (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05862638556835471
Minimum0.0
Maximum10.0
Zeros4244
Zeros (%)92.2%
Memory size36.1 KiB
2020-08-25T01:55:10.145849image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.2
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3351838298
Coefficient of variation (CV)5.717286278
Kurtosis229.2012712
Mean0.05862638557
Median Absolute Deviation (MAD)0
Skewness11.75464548
Sum269.74
Variance0.1123481997
2020-08-25T01:55:10.243628image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0424492.2%
 
0.36190.4%
 
0.05160.3%
 
0.08160.3%
 
0.17130.3%
 
0.07100.2%
 
1.1990.2%
 
0.1990.2%
 
0.0690.2%
 
0.1190.2%
 
0.170.2%
 
0.0970.2%
 
0.1670.2%
 
0.5860.1%
 
0.660.1%
 
0.8760.1%
 
1.6950.1%
 
0.250.1%
 
0.2350.1%
 
1.2750.1%
 
1.2350.1%
 
1.2650.1%
 
0.3740.1%
 
2.0640.1%
 
0.3840.1%
 
Other values (108)1663.6%
 
ValueCountFrequency (%) 
0424492.2%
 
0.011< 0.1%
 
0.022< 0.1%
 
0.032< 0.1%
 
0.042< 0.1%
 
0.05160.3%
 
0.0690.2%
 
0.07100.2%
 
0.08160.3%
 
0.0970.2%
 
ValueCountFrequency (%) 
101< 0.1%
 
5.551< 0.1%
 
5.121< 0.1%
 
4.761< 0.1%
 
4.341< 0.1%
 
3.841< 0.1%
 
3.441< 0.1%
 
3.121< 0.1%
 
2.9430.1%
 
2.851< 0.1%
 

34
Real number (ℝ≥0)

ZEROS

Distinct count177
Unique (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10541186698543796
Minimum0.0
Maximum20.0
Zeros4116
Zeros (%)89.5%
Memory size36.1 KiB
2020-08-25T01:55:10.346933image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.62
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5322598753
Coefficient of variation (CV)5.049335436
Kurtosis449.3742708
Mean0.105411867
Median Absolute Deviation (MAD)0
Skewness15.23081146
Sum485
Variance0.2833005749
2020-08-25T01:55:10.449559image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0411689.5%
 
0.1140.3%
 
0.33110.2%
 
0.24110.2%
 
0.5890.2%
 
0.580.2%
 
0.1970.2%
 
0.2970.2%
 
0.3470.2%
 
0.2670.2%
 
0.2270.2%
 
0.3160.1%
 
0.2360.1%
 
0.4460.1%
 
4.7660.1%
 
0.8660.1%
 
0.0860.1%
 
0.3960.1%
 
0.6750.1%
 
0.5450.1%
 
0.4350.1%
 
0.4250.1%
 
0.2850.1%
 
1.7250.1%
 
0.1550.1%
 
Other values (152)3207.0%
 
ValueCountFrequency (%) 
0411689.5%
 
0.011< 0.1%
 
0.032< 0.1%
 
0.041< 0.1%
 
0.0630.1%
 
0.072< 0.1%
 
0.0860.1%
 
0.0930.1%
 
0.1140.3%
 
0.111< 0.1%
 
ValueCountFrequency (%) 
201< 0.1%
 
5.881< 0.1%
 
4.7660.1%
 
4.651< 0.1%
 
4.541< 0.1%
 
4.441< 0.1%
 
4.3430.1%
 
4.1630.1%
 
4.111< 0.1%
 
41< 0.1%
 

55
Real number (ℝ≥0)

SKEWED

Distinct count271
Unique (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.17278852423386
Minimum1
Maximum9989
Zeros0
Zeros (%)0.0%
Memory size36.1 KiB
2020-08-25T01:55:10.568154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median15
Q343
95-th percentile181
Maximum9989
Range9988
Interquartile range (IQR)37

Descriptive statistics

Standard deviation194.8913095
Coefficient of variation (CV)3.735497278
Kurtosis1480.64205
Mean52.17278852
Median Absolute Deviation (MAD)11
Skewness30.76499258
Sum240047
Variance37982.62253
2020-08-25T01:55:10.667121image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
13497.6%
 
52475.4%
 
112285.0%
 
42214.8%
 
122194.8%
 
31653.6%
 
71342.9%
 
151332.9%
 
131322.9%
 
101282.8%
 
61222.7%
 
81092.4%
 
2972.1%
 
9781.7%
 
17751.6%
 
19701.5%
 
22661.4%
 
28631.4%
 
16611.3%
 
18611.3%
 
14601.3%
 
21561.2%
 
47531.2%
 
25521.1%
 
20441.0%
 
Other values (246)157834.3%
 
ValueCountFrequency (%) 
13497.6%
 
2972.1%
 
31653.6%
 
42214.8%
 
52475.4%
 
61222.7%
 
71342.9%
 
81092.4%
 
9781.7%
 
101282.8%
 
ValueCountFrequency (%) 
99891< 0.1%
 
22041< 0.1%
 
20421< 0.1%
 
15051< 0.1%
 
14881< 0.1%
 
13331< 0.1%
 
13271< 0.1%
 
13252< 0.1%
 
117730.1%
 
11712< 0.1%
 

27
Real number (ℝ≥0)

ZEROS

Distinct count200
Unique (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12484459900021737
Minimum0.0
Maximum9.09
Zeros4138
Zeros (%)89.9%
Memory size36.1 KiB
2020-08-25T01:55:10.768364image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.8
Maximum9.09
Range9.09
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.538576044
Coefficient of variation (CV)4.313971516
Kurtosis58.37302181
Mean0.124844599
Median Absolute Deviation (MAD)0
Skewness6.606533933
Sum574.41
Variance0.2900641552
2020-08-25T01:55:10.873431image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0413889.9%
 
0.2470.2%
 
0.6870.2%
 
4.7670.2%
 
2.0470.2%
 
0.6660.1%
 
0.6360.1%
 
0.1560.1%
 
0.5460.1%
 
0.5860.1%
 
0.3950.1%
 
0.8850.1%
 
0.150.1%
 
0.2950.1%
 
4.3450.1%
 
0.6450.1%
 
0.550.1%
 
0.5950.1%
 
2.3240.1%
 
0.6740.1%
 
0.4940.1%
 
0.8540.1%
 
0.5540.1%
 
0.7440.1%
 
0.1740.1%
 
Other values (175)3377.3%
 
ValueCountFrequency (%) 
0413889.9%
 
0.022< 0.1%
 
0.031< 0.1%
 
0.0430.1%
 
0.0530.1%
 
0.0630.1%
 
0.0830.1%
 
0.092< 0.1%
 
0.150.1%
 
0.111< 0.1%
 
ValueCountFrequency (%) 
9.091< 0.1%
 
8.331< 0.1%
 
5.881< 0.1%
 
4.7670.2%
 
4.651< 0.1%
 
4.541< 0.1%
 
4.441< 0.1%
 
4.3450.1%
 
4.251< 0.1%
 
4.1630.1%
 

31
Real number (ℝ≥0)

ZEROS

Distinct count106
Unique (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.047048467724407746
Minimum0.0
Maximum4.76
Zeros4396
Zeros (%)95.5%
Memory size36.1 KiB
2020-08-25T01:55:10.974413image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4.76
Range4.76
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3285588817
Coefficient of variation (CV)6.983413012
Kurtosis127.3765293
Mean0.04704846772
Median Absolute Deviation (MAD)0
Skewness10.54918417
Sum216.47
Variance0.1079509387
2020-08-25T01:55:11.070698image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0439695.5%
 
0.5870.2%
 
4.7660.1%
 
0.6850.1%
 
0.3950.1%
 
0.6340.1%
 
0.2440.1%
 
0.5540.1%
 
0.3540.1%
 
0.1740.1%
 
0.1540.1%
 
0.7640.1%
 
1.0130.1%
 
0.7330.1%
 
0.6430.1%
 
0.8630.1%
 
0.2830.1%
 
0.4730.1%
 
0.3330.1%
 
0.5130.1%
 
4.1630.1%
 
0.8730.1%
 
4.3430.1%
 
0.2730.1%
 
0.6630.1%
 
Other values (81)1152.5%
 
ValueCountFrequency (%) 
0439695.5%
 
0.092< 0.1%
 
0.11< 0.1%
 
0.131< 0.1%
 
0.1540.1%
 
0.161< 0.1%
 
0.1740.1%
 
0.192< 0.1%
 
0.22< 0.1%
 
0.222< 0.1%
 
ValueCountFrequency (%) 
4.7660.1%
 
4.71< 0.1%
 
4.541< 0.1%
 
4.3430.1%
 
4.1630.1%
 
41< 0.1%
 
3.841< 0.1%
 
3.571< 0.1%
 
3.171< 0.1%
 
3.121< 0.1%
 

45
Real number (ℝ≥0)

ZEROS

Distinct count227
Unique (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17982395131493156
Minimum0.0
Maximum22.05
Zeros4084
Zeros (%)88.8%
Memory size36.1 KiB
2020-08-25T01:55:11.172156image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.02
Maximum22.05
Range22.05
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9111190632
Coefficient of variation (CV)5.066728078
Kurtosis150.8998167
Mean0.1798239513
Median Absolute Deviation (MAD)0
Skewness10.12266272
Sum827.37
Variance0.8301379473
2020-08-25T01:55:11.267577image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0408488.8%
 
0.08180.4%
 
0.1130.3%
 
0.09100.2%
 
0.27100.2%
 
0.1680.2%
 
0.3480.2%
 
0.3370.2%
 
0.2860.1%
 
0.860.1%
 
550.1%
 
1.250.1%
 
0.3750.1%
 
1.5850.1%
 
0.5150.1%
 
0.2550.1%
 
2.3250.1%
 
0.4450.1%
 
1.8540.1%
 
0.8940.1%
 
0.6140.1%
 
7.1440.1%
 
0.2940.1%
 
0.4840.1%
 
0.4640.1%
 
Other values (202)3637.9%
 
ValueCountFrequency (%) 
0408488.8%
 
0.021< 0.1%
 
0.031< 0.1%
 
0.042< 0.1%
 
0.0630.1%
 
0.071< 0.1%
 
0.08180.4%
 
0.09100.2%
 
0.1130.3%
 
0.1130.1%
 
ValueCountFrequency (%) 
22.051< 0.1%
 
16.71< 0.1%
 
15.351< 0.1%
 
13.371< 0.1%
 
102< 0.1%
 
9.521< 0.1%
 
9.0930.1%
 
8.491< 0.1%
 
7.881< 0.1%
 
7.6930.1%
 

12
Real number (ℝ≥0)

ZEROS

Distinct count158
Unique (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09392958052597261
Minimum0.0
Maximum5.55
Zeros3749
Zeros (%)81.5%
Memory size36.1 KiB
2020-08-25T01:55:11.369760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.61
Maximum5.55
Range5.55
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3010358036
Coefficient of variation (CV)3.204909485
Kurtosis84.94182188
Mean0.09392958053
Median Absolute Deviation (MAD)0
Skewness6.955548227
Sum432.17
Variance0.09062255504
2020-08-25T01:55:11.468129image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0374981.5%
 
0.17430.9%
 
0.19300.7%
 
0.3290.6%
 
0.32260.6%
 
0.27200.4%
 
0.12180.4%
 
0.25170.4%
 
0.29160.3%
 
0.2160.3%
 
0.65150.3%
 
0.08150.3%
 
0.38150.3%
 
0.14150.3%
 
0.31140.3%
 
0.11140.3%
 
0.16130.3%
 
0.06120.3%
 
0.09120.3%
 
0.22120.3%
 
0.28120.3%
 
0.54120.3%
 
0.37120.3%
 
0.56110.2%
 
0.15110.2%
 
Other values (133)4429.6%
 
ValueCountFrequency (%) 
0374981.5%
 
0.011< 0.1%
 
0.0230.1%
 
0.0350.1%
 
0.0440.1%
 
0.0550.1%
 
0.06120.3%
 
0.0760.1%
 
0.08150.3%
 
0.09120.3%
 
ValueCountFrequency (%) 
5.5530.1%
 
2.941< 0.1%
 
2.711< 0.1%
 
2.631< 0.1%
 
2.581< 0.1%
 
2.561< 0.1%
 
2.51< 0.1%
 
2.461< 0.1%
 
2.381< 0.1%
 
2.221< 0.1%
 

47
Real number (ℝ≥0)

ZEROS

Distinct count106
Unique (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03186915887850467
Minimum0.0
Maximum10.0
Zeros4398
Zeros (%)95.6%
Memory size36.1 KiB
2020-08-25T01:55:11.575016image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2857346463
Coefficient of variation (CV)8.965867201
Kurtosis537.4930074
Mean0.03186915888
Median Absolute Deviation (MAD)0
Skewness19.72044578
Sum146.63
Variance0.08164428809
2020-08-25T01:55:11.670989image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0439895.6%
 
0.13100.2%
 
0.2470.2%
 
0.270.2%
 
0.1560.1%
 
0.1960.1%
 
0.160.1%
 
0.2850.1%
 
0.1450.1%
 
0.0440.1%
 
0.0840.1%
 
0.3340.1%
 
0.1140.1%
 
0.3430.1%
 
0.4430.1%
 
0.3530.1%
 
0.0930.1%
 
0.1230.1%
 
0.0530.1%
 
0.0230.1%
 
0.4130.1%
 
0.32< 0.1%
 
0.542< 0.1%
 
0.262< 0.1%
 
0.532< 0.1%
 
Other values (81)1032.2%
 
ValueCountFrequency (%) 
0439895.6%
 
0.0230.1%
 
0.031< 0.1%
 
0.0440.1%
 
0.0530.1%
 
0.061< 0.1%
 
0.072< 0.1%
 
0.0840.1%
 
0.0930.1%
 
0.160.1%
 
ValueCountFrequency (%) 
101< 0.1%
 
8.331< 0.1%
 
51< 0.1%
 
4.761< 0.1%
 
3.71< 0.1%
 
3.332< 0.1%
 
2.811< 0.1%
 
2.562< 0.1%
 
2.51< 0.1%
 
2.451< 0.1%
 

3
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct count43
Unique (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06542490762877635
Minimum0.0
Maximum42.81
Zeros4554
Zeros (%)99.0%
Memory size36.1 KiB
2020-08-25T01:55:11.774554image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum42.81
Range42.81
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.39515137
Coefficient of variation (CV)21.32446833
Kurtosis726.4515381
Mean0.06542490763
Median Absolute Deviation (MAD)0
Skewness26.22774447
Sum301.02
Variance1.946447347
2020-08-25T01:55:11.867632image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0455499.0%
 
35.462< 0.1%
 
0.582< 0.1%
 
0.212< 0.1%
 
0.422< 0.1%
 
0.172< 0.1%
 
0.521< 0.1%
 
0.111< 0.1%
 
1.261< 0.1%
 
0.041< 0.1%
 
0.061< 0.1%
 
19.731< 0.1%
 
9.161< 0.1%
 
0.151< 0.1%
 
0.11< 0.1%
 
0.491< 0.1%
 
0.951< 0.1%
 
0.191< 0.1%
 
1.291< 0.1%
 
0.441< 0.1%
 
7.071< 0.1%
 
7.181< 0.1%
 
0.811< 0.1%
 
1.351< 0.1%
 
0.911< 0.1%
 
Other values (18)180.4%
 
ValueCountFrequency (%) 
0455499.0%
 
0.041< 0.1%
 
0.061< 0.1%
 
0.11< 0.1%
 
0.111< 0.1%
 
0.131< 0.1%
 
0.141< 0.1%
 
0.151< 0.1%
 
0.161< 0.1%
 
0.172< 0.1%
 
ValueCountFrequency (%) 
42.811< 0.1%
 
42.731< 0.1%
 
40.131< 0.1%
 
35.462< 0.1%
 
19.731< 0.1%
 
19.161< 0.1%
 
13.631< 0.1%
 
9.161< 0.1%
 
7.181< 0.1%
 
7.071< 0.1%
 

43
Real number (ℝ≥0)

ZEROS

Distinct count160
Unique (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0791958269941317
Minimum0.0
Maximum20.0
Zeros4274
Zeros (%)92.9%
Memory size36.1 KiB
2020-08-25T01:55:11.969898image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.24
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6219755735
Coefficient of variation (CV)7.853640742
Kurtosis479.8309068
Mean0.07919582699
Median Absolute Deviation (MAD)0
Skewness18.7715155
Sum364.38
Variance0.386853614
2020-08-25T01:55:12.076832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0427492.9%
 
0.08130.3%
 
0.06120.3%
 
0.05100.2%
 
0.28100.2%
 
0.190.2%
 
0.3380.2%
 
0.1680.2%
 
0.0270.2%
 
0.860.1%
 
0.2660.1%
 
0.5450.1%
 
0.5840.1%
 
0.1440.1%
 
0.0940.1%
 
0.0340.1%
 
0.2930.1%
 
0.4430.1%
 
4.5430.1%
 
1.2630.1%
 
0.2230.1%
 
0.3730.1%
 
0.2330.1%
 
0.8430.1%
 
0.6430.1%
 
Other values (135)1904.1%
 
ValueCountFrequency (%) 
0427492.9%
 
0.011< 0.1%
 
0.0270.2%
 
0.0340.1%
 
0.042< 0.1%
 
0.05100.2%
 
0.06120.3%
 
0.071< 0.1%
 
0.08130.3%
 
0.0940.1%
 
ValueCountFrequency (%) 
201< 0.1%
 
16.662< 0.1%
 
101< 0.1%
 
8.11< 0.1%
 
6.341< 0.1%
 
6.251< 0.1%
 
52< 0.1%
 
4.761< 0.1%
 
4.571< 0.1%
 
4.5430.1%
 

26
Real number (ℝ≥0)

ZEROS

Distinct count240
Unique (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.767304933710063
Minimum0.0
Maximum33.33
Zeros3821
Zeros (%)83.0%
Memory size36.1 KiB
2020-08-25T01:55:12.191788image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.73
Maximum33.33
Range33.33
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.367291802
Coefficient of variation (CV)4.388466247
Kurtosis34.20447596
Mean0.7673049337
Median Absolute Deviation (MAD)0
Skewness5.744493294
Sum3530.37
Variance11.33865408
2020-08-25T01:55:12.298054image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0382183.0%
 
20801.7%
 
25160.3%
 
0.05150.3%
 
0.7130.3%
 
0.08100.2%
 
16.66100.2%
 
290.2%
 
4.7690.2%
 
0.6870.2%
 
4.3470.2%
 
1.1770.2%
 
2.6370.2%
 
3.0370.2%
 
0.1170.2%
 
4.1670.2%
 
0.8860.1%
 
1.460.1%
 
1.6360.1%
 
1.3660.1%
 
1.260.1%
 
14.2860.1%
 
2.5660.1%
 
1.1660.1%
 
1.1960.1%
 
Other values (215)51511.2%
 
ValueCountFrequency (%) 
0382183.0%
 
0.0130.1%
 
0.0230.1%
 
0.0330.1%
 
0.0430.1%
 
0.05150.3%
 
0.0650.1%
 
0.0750.1%
 
0.08100.2%
 
0.0940.1%
 
ValueCountFrequency (%) 
33.3340.1%
 
25160.3%
 
20801.7%
 
16.66100.2%
 
14.2860.1%
 
13.331< 0.1%
 
12.52< 0.1%
 
11.1140.1%
 
101< 0.1%
 
9.0940.1%
 

8
Real number (ℝ≥0)

ZEROS

Distinct count144
Unique (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09006737665724841
Minimum0.0
Maximum5.26
Zeros3828
Zeros (%)83.2%
Memory size36.1 KiB
2020-08-25T01:55:12.406175image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.65
Maximum5.26
Range5.26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2786158642
Coefficient of variation (CV)3.093416002
Kurtosis46.94025552
Mean0.09006737666
Median Absolute Deviation (MAD)0
Skewness5.226066951
Sum414.4
Variance0.07762679981
2020-08-25T01:55:12.519267image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0382883.2%
 
0.09260.6%
 
0.08240.5%
 
0.8180.4%
 
0.1170.4%
 
0.23160.3%
 
0.16160.3%
 
0.24150.3%
 
0.05150.3%
 
0.2140.3%
 
0.06140.3%
 
0.44130.3%
 
0.59130.3%
 
0.4130.3%
 
0.32130.3%
 
0.54130.3%
 
0.28120.3%
 
0.58120.3%
 
0.29120.3%
 
0.27120.3%
 
0.31110.2%
 
0.66110.2%
 
0.25110.2%
 
0.13100.2%
 
0.35100.2%
 
Other values (119)4329.4%
 
ValueCountFrequency (%) 
0382883.2%
 
0.011< 0.1%
 
0.022< 0.1%
 
0.0340.1%
 
0.0440.1%
 
0.05150.3%
 
0.06140.3%
 
0.0770.2%
 
0.08240.5%
 
0.09260.6%
 
ValueCountFrequency (%) 
5.261< 0.1%
 
3.331< 0.1%
 
3.231< 0.1%
 
2.591< 0.1%
 
2.51< 0.1%
 
2.481< 0.1%
 
2.381< 0.1%
 
2.351< 0.1%
 
2.291< 0.1%
 
2.121< 0.1%
 

17
Real number (ℝ≥0)

ZEROS

Distinct count229
Unique (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18474462073462292
Minimum0.0
Maximum9.09
Zeros3563
Zeros (%)77.4%
Memory size36.1 KiB
2020-08-25T01:55:12.633208image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.21
Maximum9.09
Range9.09
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.531122422
Coefficient of variation (CV)2.874900605
Kurtosis47.96167444
Mean0.1847446207
Median Absolute Deviation (MAD)0
Skewness5.413753723
Sum850.01
Variance0.2820910272
2020-08-25T01:55:12.742347image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0356377.4%
 
0.08200.4%
 
0.05200.4%
 
1.11200.4%
 
0.32180.4%
 
0.44170.4%
 
0.06170.4%
 
0.33160.3%
 
0.12150.3%
 
0.19140.3%
 
0.38130.3%
 
0.55130.3%
 
0.03130.3%
 
0.9120.3%
 
0.36120.3%
 
0.07120.3%
 
0.29120.3%
 
0.27110.2%
 
0.46110.2%
 
0.34110.2%
 
0.39110.2%
 
0.58110.2%
 
0.4110.2%
 
0.11100.2%
 
0.37100.2%
 
Other values (204)70815.4%
 
ValueCountFrequency (%) 
0356377.4%
 
0.0140.1%
 
0.0270.2%
 
0.03130.3%
 
0.0440.1%
 
0.05200.4%
 
0.06170.4%
 
0.07120.3%
 
0.08200.4%
 
0.092< 0.1%
 
ValueCountFrequency (%) 
9.091< 0.1%
 
7.691< 0.1%
 
6.661< 0.1%
 
5.331< 0.1%
 
5.261< 0.1%
 
5.061< 0.1%
 
4.871< 0.1%
 
4.831< 0.1%
 
4.511< 0.1%
 
4.441< 0.1%
 

6
Real number (ℝ≥0)

ZEROS

Distinct count173
Unique (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11420778091719191
Minimum0.0
Maximum7.27
Zeros3794
Zeros (%)82.5%
Memory size36.1 KiB
2020-08-25T01:55:12.846341image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.74
Maximum7.27
Range7.27
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3914413548
Coefficient of variation (CV)3.42744909
Kurtosis75.41343865
Mean0.1142077809
Median Absolute Deviation (MAD)0
Skewness6.765580469
Sum525.47
Variance0.1532263342
2020-08-25T01:55:13.136960image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0379482.5%
 
0.08300.7%
 
0.05210.5%
 
0.5190.4%
 
0.32190.4%
 
0.19180.4%
 
0.25160.3%
 
0.4140.3%
 
0.16140.3%
 
0.1140.3%
 
0.2130.3%
 
0.06120.3%
 
0.33120.3%
 
0.31120.3%
 
0.17120.3%
 
0.68110.2%
 
0.03110.2%
 
0.23110.2%
 
0.38110.2%
 
0.49110.2%
 
0.14110.2%
 
0.09100.2%
 
0.15100.2%
 
0.990.2%
 
0.6490.2%
 
Other values (148)47710.4%
 
ValueCountFrequency (%) 
0379482.5%
 
0.0240.1%
 
0.03110.2%
 
0.0480.2%
 
0.05210.5%
 
0.06120.3%
 
0.0770.2%
 
0.08300.7%
 
0.09100.2%
 
0.1140.3%
 
ValueCountFrequency (%) 
7.272< 0.1%
 
5.41< 0.1%
 
4.541< 0.1%
 
4.081< 0.1%
 
41< 0.1%
 
3.271< 0.1%
 
3.122< 0.1%
 
3.071< 0.1%
 
2.981< 0.1%
 
2.942< 0.1%
 

4
Real number (ℝ≥0)

ZEROS

Distinct count255
Unique (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.312223429689198
Minimum0.0
Maximum10.0
Zeros2853
Zeros (%)62.0%
Memory size36.1 KiB
2020-08-25T01:55:13.252497image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.38
95-th percentile1.49
Maximum10
Range10
Interquartile range (IQR)0.38

Descriptive statistics

Standard deviation0.6725127693
Coefficient of variation (CV)2.153947159
Kurtosis37.94116889
Mean0.3122234297
Median Absolute Deviation (MAD)0
Skewness4.747126114
Sum1436.54
Variance0.4522734249
2020-08-25T01:55:13.354497image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0285362.0%
 
0.36280.6%
 
0.32260.6%
 
0.19240.5%
 
0.8240.5%
 
0.26220.5%
 
0.29220.5%
 
0.14220.5%
 
0.23210.5%
 
0.68210.5%
 
0.53210.5%
 
0.64210.5%
 
0.45210.5%
 
0.34210.5%
 
0.4200.4%
 
0.13200.4%
 
0.65200.4%
 
0.38190.4%
 
0.33190.4%
 
0.27190.4%
 
0.09190.4%
 
0.25190.4%
 
0.43190.4%
 
0.12190.4%
 
0.28180.4%
 
Other values (230)124327.0%
 
ValueCountFrequency (%) 
0285362.0%
 
0.0250.1%
 
0.031< 0.1%
 
0.04120.3%
 
0.05150.3%
 
0.062< 0.1%
 
0.0790.2%
 
0.08150.3%
 
0.09190.4%
 
0.1170.4%
 
ValueCountFrequency (%) 
101< 0.1%
 
9.091< 0.1%
 
8.331< 0.1%
 
7.691< 0.1%
 
7.141< 0.1%
 
6.2540.1%
 
5.551< 0.1%
 
5.261< 0.1%
 
52< 0.1%
 
4.7630.1%
 

40
Real number (ℝ≥0)

ZEROS

Distinct count108
Unique (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.043666594218648117
Minimum0.0
Maximum7.14
Zeros4453
Zeros (%)96.8%
Memory size36.1 KiB
2020-08-25T01:55:13.464301image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7.14
Range7.14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3612047007
Coefficient of variation (CV)8.271877098
Kurtosis193.6192939
Mean0.04366659422
Median Absolute Deviation (MAD)0
Skewness12.58790037
Sum200.91
Variance0.1304688358
2020-08-25T01:55:13.572837image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0445396.8%
 
0.3150.1%
 
7.1440.1%
 
4.7530.1%
 
0.130.1%
 
0.3430.1%
 
1.4430.1%
 
0.2530.1%
 
0.682< 0.1%
 
2.562< 0.1%
 
1.922< 0.1%
 
0.892< 0.1%
 
0.462< 0.1%
 
0.362< 0.1%
 
1.162< 0.1%
 
22< 0.1%
 
0.082< 0.1%
 
0.062< 0.1%
 
0.282< 0.1%
 
1.062< 0.1%
 
0.652< 0.1%
 
0.132< 0.1%
 
0.512< 0.1%
 
0.162< 0.1%
 
0.862< 0.1%
 
Other values (83)902.0%
 
ValueCountFrequency (%) 
0445396.8%
 
0.021< 0.1%
 
0.031< 0.1%
 
0.041< 0.1%
 
0.062< 0.1%
 
0.071< 0.1%
 
0.082< 0.1%
 
0.130.1%
 
0.132< 0.1%
 
0.162< 0.1%
 
ValueCountFrequency (%) 
7.1440.1%
 
5.881< 0.1%
 
51< 0.1%
 
4.761< 0.1%
 
4.7530.1%
 
4.281< 0.1%
 
3.261< 0.1%
 
3.251< 0.1%
 
3.221< 0.1%
 
3.121< 0.1%
 

target
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.1 KiB
0
2788
1
1813
ValueCountFrequency (%) 
0278860.6%
 
1181339.4%
 

Interactions

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2020-08-25T01:54:46.216269image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:46.340275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:46.474615image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:46.607347image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:46.744270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:46.865854image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:46.991407image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:47.307964image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:47.447748image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:47.596633image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:47.720875image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:47.849569image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:47.977695image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:48.108986image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:48.233502image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:48.362413image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:48.484363image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:48.622935image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:48.750154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:48.874904image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:48.995783image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:49.129705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:49.251988image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:49.379412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:49.521274image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:49.673699image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:49.824775image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:49.972456image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:50.136815image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:50.283303image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:50.424640image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:50.569455image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:50.717926image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:50.864999image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:51.013011image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:51.161925image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:51.320248image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:51.469184image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:51.623079image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:51.950015image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:52.102740image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:52.249193image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:52.397689image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:52.533569image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:52.673134image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:52.804873image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:52.933542image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:53.082394image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:53.218623image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:53.348325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:53.479131image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:53.615051image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:53.751213image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:53.887834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:54.019262image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:54.168702image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:54.308544image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:54.444280image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:54.571603image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:54.712867image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:54.846632image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:54.992670image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:55.116696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:55.249980image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:55.397099image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:55.524268image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:55.664128image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:55.799756image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:55.925045image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:56.048578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:56.175106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:56.501845image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:56.637992image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:56.798937image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:56.983562image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:57.131808image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:57.262503image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:57.387372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:57.521318image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:57.647322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:57.782302image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:57.907848image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:58.034167image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:58.162317image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:58.289144image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:58.424338image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:58.550507image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:58.670351image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:58.791777image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:58.911486image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:59.035937image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:59.166972image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:59.301522image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:59.436758image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:59.570703image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:59.700327image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:59.824621image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:54:59.950215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:00.072158image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:00.201554image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:00.348886image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:00.498938image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:00.639009image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:00.785221image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:01.138452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:01.277661image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:01.416946image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:01.552924image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:01.686724image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:01.821327image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:01.962230image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:02.097178image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:02.253199image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:02.404181image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:02.542670image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:02.683149image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:02.828928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:02.967305image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:03.113656image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:03.248609image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:03.387499image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:03.518528image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:03.651958image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:03.791690image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:03.925610image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:04.059591image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:04.189081image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:04.318104image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:04.447652image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:04.577639image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:04.703632image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:04.845143image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:04.977627image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:05.106071image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:05.232307image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:05.368327image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:05.686596image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:05.820606image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:05.956557image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:06.099245image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:06.241941image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:06.378473image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:06.528708image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:06.664793image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:06.798191image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:06.933548image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:07.068199image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:07.203839image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:07.342073image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:07.478102image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:07.637121image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:07.777359image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:07.915833image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:08.050743image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:08.198519image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:08.338868image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T01:55:13.714320image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T01:55:14.022432image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T01:55:14.313753image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T01:55:14.605638image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T01:55:08.635373image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:55:09.103272image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

05301334552731451247343268176440target
00.000.000.00.000.0610.00.00.000.000.00.00.000.00.001.290.000.320.01
10.210.280.00.210.01010.00.00.000.650.00.00.000.00.000.280.210.140.01
20.060.190.00.000.04850.00.00.060.120.00.00.000.00.641.030.191.230.01
30.000.000.00.000.0400.00.00.000.310.00.00.000.00.310.000.310.630.01
40.000.000.00.000.0400.00.00.000.310.00.00.000.00.310.000.310.630.01
50.000.000.00.000.0150.00.00.000.000.00.00.000.00.000.000.001.850.01
60.000.000.00.000.040.00.00.000.000.00.00.000.00.000.320.001.920.01
70.000.000.00.000.0110.00.00.000.000.00.00.000.00.000.000.001.880.01
80.150.000.00.000.04450.00.00.000.000.00.00.000.00.920.150.300.610.01
90.060.320.00.000.0430.00.00.000.250.00.00.060.00.060.120.380.190.01

Last rows

05301334552731451247343268176440target
45910.000.000.00.00.010.00.03.440.000.00.00.000.00.00.000.00.000.000
45920.000.000.00.00.040.00.00.620.620.00.01.250.00.00.000.02.500.620
45930.000.000.00.00.010.00.07.690.000.00.00.000.00.00.000.00.000.000
45940.000.000.00.00.050.00.01.610.000.00.00.000.00.00.000.00.000.000
45950.000.000.00.00.010.00.00.590.000.00.00.000.00.00.590.00.000.000
45960.310.310.00.00.030.00.00.310.000.00.00.310.00.00.000.00.000.000
45970.000.000.00.00.040.00.02.000.000.00.00.000.00.00.000.00.000.000
45980.300.000.00.00.060.00.01.200.300.00.00.000.00.00.900.00.000.000
45990.960.000.00.00.050.00.00.320.000.00.00.320.00.00.000.00.320.000
46000.000.000.00.00.050.00.00.650.650.00.00.000.00.00.000.00.000.000

Duplicate rows

Most frequent

05301334552731451247343268176440targetcount
00.00.00.00.00.010.00.00.00.00.00.00.00.00.00.00.00.00.00127
300.00.00.00.00.050.00.00.00.00.00.00.00.00.00.00.00.00.0097
230.00.00.00.00.040.00.00.00.00.00.00.00.00.00.00.00.00.0082
90.00.00.00.00.010.00.00.00.00.00.00.020.00.00.00.00.00.0075
190.00.00.00.00.030.00.00.00.00.00.00.00.00.00.00.00.00.0064
150.00.00.00.00.020.00.00.00.00.00.00.00.00.00.00.00.00.0039
370.00.00.00.00.060.00.00.00.00.00.00.00.00.00.00.00.00.0037
410.00.00.00.00.070.00.00.00.00.00.00.00.00.00.00.00.00.0036
480.00.00.00.00.080.00.00.00.00.00.00.00.00.00.00.00.00.0036
550.00.00.00.00.090.00.00.00.00.00.00.00.00.00.00.00.00.0028